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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

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MethaneMapper: Spectral Absorption Aware Hyperspectral Transformer for Methane Detection

MethaneMapper: Spectral Absorption Aware Hyperspectral Trans...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Satish Kumar Ivan Arevalo ASM Iftekhar B S Manjunath Department of Electrical and Computer Engineering University of California Santa Barbara

Methane (CH 4 ) is the chief contributor to global climate change. Recent Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) has been very useful in quantitative mapping of methane emissions. Existing methods for analyzing this data are sensitive to local terrain conditions, often require manual inspection from domain experts, prone to significant error and hence are not scalable. To address these challenges, we propose a novel end-to-end spectral absorption wavelength aware transformer network, MethaneMapper, to detect and quantify the emissions. MethaneMapper introduces two novel modules that help to locate the most relevant methane plume regions in the spectral domain and uses them to localize these accurately. Thorough evaluation shows that MethaneMapper achieves 0.63 mAP in detection and reduces the model size (by 5×) compared to the current state of the art. In addition, we also introduce a large-scale dataset of methane plume segmentation mask for over 1200 AVIRIS-NG flight lines from 2015–2022. It contains over 4000 methane plume sites. Our dataset will provide researchers the opportunity to develop and advance new methods for tackling this challenging green-house gas detection problem with significant broader social impact. Dataset and source code link 1 1 https://***/UCSB-Vrl/methaneMapper-Spectral-Absorption-aware-Hyperspectral-Transformer-for-Methane-Detection.

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Prototype-Guided Saliency Feature Learning for Person Search

Prototype-Guided Saliency Feature Learning for Person Search

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kim, Hanjae Joung, Sunghun Kim, Ig-Jae Sohn, Kwanghoon Yonsei Univ Seoul South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (纸本)9781665445092

Existing person search methods integrate person detection and re-identification (re-ID) module into a unified system. Though promising results have been achieved, the misalignment problem, which commonly occurs in person search, limits the discriminative feature representation for re-ID. To overcome this limitation, we introduce a novel framework to learn the discriminative representation by utilizing prototype in OIM loss. Unlike conventional methods using prototype as a representation of person identity, we utilize it as guidance to allow the attention network to consistently highlight multiple instances across different poses. Moreover, we propose a new prototype update scheme with adaptive momentum to increase the discriminative ability across different instances. Extensive ablation experiments demonstrate that our method can significantly enhance the feature discriminative power, outperforming the state-of-the-art results on two person search benchmarks including CUHK-SYSU and PRW.

关键词： computer vision Adaptation models Annotations Computational modeling Prototypes Benchmark testing Search problems

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Few-shot Open-set recognition by Transformation Consistency

Few-shot Open-set Recognition by Transformation Consistency

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Jeong, Minki Choi, Seokeon Kim, Changick Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt a model to a given small set of labeled samples while rejecting unseen class samples. Since OSR requires rich data and FSL considers closed-set classification, existing OSR and FSL methods show poor performances in solving FSOSR problems. The previous FSOSR method follows the pseudo-unseen class sample-based methods, which collect pseudo-unseen samples from the other dataset or synthesize samples to model unseen class representations. However, this approach is heavily dependent on the composition of the pseudo samples. In this paper, we propose a novel unknown class sample detector, named SnaTCHer, that does not require pseudo-unseen samples. Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set. The modified set is composed by replacing a query feature and its predicted class prototype. SnaTCHer rejects samples with large differences to the transformed prototypes. Our method alters the unseen class distribution estimation problem to a relative feature transformation problem, independent of pseudo-unseen class samples. We investigate our SnaTCHer with various prototype transformation methods and observe that our method consistently improves unseen class sample detection performance without closed-set classification reduction.

关键词： Learning systems computer vision Adaptation models Prototypes Estimation Detectors pattern recognition

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CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST: A Class-Rebalancing Self-Training Framework for Imbal...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wei, Chen Sohn, Kihyuk Mellina, Clayton Yuille, Alan Yang, Fan Johns Hopkins Univ Baltimore MD 21218 USA Google Cloud AI Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploiting this property, in this work, we propose Class-Rebalancing Self-Training (CReST), a simple yet effective framework to improve existing SSL methods on class-imbalanced data. CReST iteratively retrains a baseline SSL model with a labeled set expanded by adding pseudo labeled samples from an unlabeled set, where pseudo labeled samples from minority classes are selected more frequently according to an estimated class distribution. We also propose a progressive distribution alignment to adaptively adjust the rebalancing strength dubbed CReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms on various class-imbalanced datasets and consistently outperform other popular rebalancing methods. Code has been made available at ht-sps//***/google-research/crest.

关键词： computer vision Adaptation models Codes Semisupervised learning pattern recognition

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OTA: Optimal Transport Assignment for Object Detection

OTA: Optimal Transport Assignment for Object Detection

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ge, Zheng Liu, Songtao Liu, Zeming Yoshie, Osamu Sun, Jian Waseda Univ Tokyo Japan Megvii Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we innovatively revisit the label assignment from a global perspective and propose to formulate the assigning procedure as an Optimal Transport (OT) problem - a well-studied topic in Optimization Theory. Concretely, we define the unit transportation cost between each demander (anchor) and supplier (gt) pair as the weighted summation of their classification and regression losses. After formulation, finding the best assignment solution is converted to solve the optimal transport plan at minimal transportation costs, which can be solved via Sinkhorn-Knopp Iteration. On COCO, a single FCOS-ResNet-50 detector equipped with Optimal Transport Assignment (OTA) can reach 40.7% mAP under 1x scheduler, outperforming all other existing assigning methods. Extensive experiments conducted on COCO and CrowdHuman further validate the effectiveness of our proposed OTA, especially its superiority in crowd scenarios.

关键词： Training computer vision Costs Codes Transportation Estimation Object detection

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Towards Realistic Long-Tailed Semi-Supervised Learning: Consistency is All You Need

Towards Realistic Long-Tailed Semi-Supervised Learning: Cons...

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conference on computer vision and pattern recognition (cvpr)

作者： Tong Wei Kai Gan School of Computer Science and Engineering Southeast University Nanjing China

While long-tailed semi-supervised learning (LTSSL) has received tremendous attention in many real-world classification problems, existing LTSSL algorithms typically assume that the class distributions of labeled and unlabeled data are almost identical. Those LTSSL algorithms built upon the assumption can severely suffer when the class distributions of labeled and unlabeled data are mismatched since they utilize biased pseudo-labels from the model. To alleviate this issue, we propose a new simple method that can effectively utilize unlabeled data of unknown class distributions by introducing the adaptive consistency regularizer (ACR). ACR realizes the dynamic refinery of pseudolabels for various distributions in a unified formula by estimating the true class distribution of unlabeled data. Despite its simplicity, we show that ACR achieves state-of-the-art performance on a variety of standard LTSSL benchmarks, e.g., an averaged 10% absolute increase of test accuracy against existing algorithms when the class distributions of labeled and unlabeled data are mismatched. Even when the class distributions are identical, ACR consistently outper-forms many sophisticated LTSSL algorithms. We carry out extensive ablation studies to tease apart the factors that are most important to ACR's success. Source code is available at https://***/Gank0078/ACR.

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Robust Test-Time Adaptation in Dynamic Scenarios

Robust Test-Time Adaptation in Dynamic Scenarios

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conference on computer vision and pattern recognition (cvpr)

作者： Longhui Yuan Binhui Xie Shuang Li School of Computer Science and Technology Beijing Institute of Technology

Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with only unlabeled test data streams. Most of the previous TTA methods have achieved great success on simple test data streams such as independently sampled data from single or multiple distributions. However, these attempts may fail in dynamic scenarios of real-world applications like autonomous driving, where the environments gradually change and the test data is sampled correlatively over time. In this work, we explore such practical test data streams to deploy the model on the fly, namely practical test-time adaptation (PTTA). To do so, we elaborate a Robust Test-Time Adaptation (RoTTA) method against the complex data stream in PTTA. More specifically, we present a robust batch normalization scheme to estimate the normalization statistics. Meanwhile, a memory bank is utilized to sample category-balanced data with consideration of timeliness and uncertainty. Further, to stabilize the training procedure, we develop a time-aware reweighting strategy with a teacher-student model. Extensive experiments prove that RoTTA enables continual test-time adaptation on the correlatively sampled data streams. Our method is easy to implement, making it a good choice for rapid deployment. The code is publicly available at https://***/BIT-DA/RoTTA

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A Paradigm Shift towards computer vision

A Paradigm Shift towards Computer Vision

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2023 ieee International conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Chaithra, N. Jha, Janhvi Sayal, Anu Gupta, Veethika Gupta, Ashulekha Karnataka Bangalore India Taylor's University Department of Mathematics Malaysia Doon University School of Social Sciences Department of Economics Uttarakhand India Department of Management Uttarakhand Dehradun India

ISBN: (纸本)9781665474924

In today's world, machine learning, artificial intelligence, IoT, deep learning and several other techniques have become the need of the moment. One such division of artificial intelligence is computer vision. The main goal of computer vision development is to create paradigms for extracting data and information from images. It has various applications in the fields of industry, agriculture, automations, healthcare, e-commerce and much more. The study examines the most recent events and conceptual frameworks governing the progress of computer vision, with a focus on pattern recognition and image processing, using a variety of applications from the t field. This article attempts to discuss the most current results and applications in computer vision. © 2023 ieee.

关键词： computer vision

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Shot Contrastive Self-Supervised Learning for Scene Boundary Detection

Shot Contrastive Self-Supervised Learning for Scene Boundary...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Shixing Nie, Xiaohan Fan, David Zhang, Dongqing Bhat, Vimal Hamid, Raffay Amazon Prime Video Seattle WA 98109 USA

ISBN: (纸本)9781665445092

Scenes play a crucial role in breaking the storyline of movies and TV episodes into semantically cohesive parts. However, given their complex temporal structure, finding scene boundaries can be a challenging task requiring large amounts of labeled training data. To address this challenge, we present a self-supervised shot contrastive learning approach (ShotCoL) to learn a shot representation that maximizes the similarity between nearby shots compared to randomly selected shots. We show how to apply our learned shot representation for the task of scene boundary detection to offer state-of-the-art performance on the MovieNet [33] dataset while requiring only similar to 25% of the training labels, using 9x fewer model parameters and offering 7x faster runtime. To assess the effectiveness of ShotCoL on novel applications of scene boundary detection, we take on the problem of finding timestamps in movies and TV episodes where video-ads can be inserted while offering a minimally disruptive viewing experience. To this end, we collected a new dataset called AdCuepoints with 3;975 movies and TV episodes, 2:2 million shots and 19;119 minimally disruptive ad cue-point labels. We present a thorough empirical analysis on this dataset demonstrating the effectiveness of ShotCoL for ad cue-points detection.

关键词： Training computer vision TV Runtime Training data Motion pictures pattern recognition

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Deep Texture recognition via Exploiting Cross-Layer Statistical Self-Similarity

Deep Texture Recognition via Exploiting Cross-Layer Statisti...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Zhile Li, Feng Quan, Yuhui Xu, Yong Ji, Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (纸本)9781665445092

In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition. The key of existing CNN-based approaches is aggregating the convolutional features into a robust yet discriminative description. This paper presents a novel feature aggregation module called CLASS (Cross-Layer Aggregation of Statistical Self-similarity) for texture recognition. We model the CNN feature maps across different layers, as a dynamic process which carries the statistical self-similarity (SSS), one well-known property of texture, from input image along the network depth dimension. The CLASS module characterizes the cross-layer SSS using a soft histogram of local differential box-counting dimensions of cross-layer features. The resulting descriptor encodes both cross-layer dynamics and local SSS of input image, providing additional discrimination over the often-used global average pooling. Integrating CLASS into a ResNet backbone, we develop CLASSNet, an effective deep model for texture recognition, which shows state-of-the-art performance in the experiments.

关键词： Histograms computer vision Computational modeling Tools pattern recognition Convolutional neural networks

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